feat: LLM accuracy testing framework with 17-test catalog

Framework runs a category/subcategory test catalog against multiple
LLMs through an OpenAI-compatible endpoint. Deterministic evaluators
score each response. Reports render as table, markdown, or JSON.

internal/report/table.go

@@ -0,0 +1,96 @@
+package report
+
+import (
+	"fmt"
+	"io"
+	"math"
+	"strings"
+	"text/tabwriter"
+
+	"github.com/lukaszraczylo/llm-testbench/internal/runner"
+	"github.com/lukaszraczylo/llm-testbench/internal/testkit"
+)
+
+// errWriter wraps an io.Writer and remembers the first write error, so a
+// sequence of Fprint* calls can be written without checking after every
+// line; the caller checks ew.err once at the end.
+type errWriter struct {
+	w   io.Writer
+	err error
+}
+
+func (ew *errWriter) println(args ...any) {
+	if ew.err != nil {
+		return
+	}
+	_, ew.err = fmt.Fprintln(ew.w, args...)
+}
+
+// renderTable writes three plain-text tables: per-test scores, category
+// rollup, and model summary.
+func renderTable(w io.Writer, tests []testkit.Test, models []string, results []runner.Result) error {
+	idx := indexResults(results)
+	tw := tabwriter.NewWriter(w, 0, 4, 2, ' ', 0)
+	ew := &errWriter{w: tw}
+
+	writeScoreTable(ew, tests, models, idx)
+	ew.println()
+	writeCategoryTable(ew, tests, models, idx)
+	ew.println()
+	writeSummaryTable(ew, models, results)
+	if ew.err != nil {
+		return ew.err
+	}
+
+	return tw.Flush()
+}
+
+func writeScoreTable(ew *errWriter, tests []testkit.Test, models []string, idx resultIndex) {
+	ew.println("== Per-test scores ==")
+	header := []string{"TEST", "CATEGORY", "SUBCATEGORY"}
+	header = append(header, models...)
+	ew.println(strings.Join(header, "\t"))
+
+	for _, t := range sortedTests(tests) {
+		row := []string{t.ID, t.Category, t.Subcategory}
+		for _, model := range models {
+			r, ok := idx[t.ID][model]
+			row = append(row, cellText(r, ok))
+		}
+		ew.println(strings.Join(row, "\t"))
+	}
+}
+
+func writeCategoryTable(ew *errWriter, tests []testkit.Test, models []string, idx resultIndex) {
+	ew.println("== Category rollup (mean score) ==")
+	header := append([]string{"CATEGORY"}, models...)
+	ew.println(strings.Join(header, "\t"))
+
+	for _, cm := range categoryRollup(tests, models, idx) {
+		row := []string{cm.category}
+		for _, model := range models {
+			row = append(row, meanCellText(cm.means[model]))
+		}
+		ew.println(strings.Join(row, "\t"))
+	}
+}
+
+func writeSummaryTable(ew *errWriter, models []string, results []runner.Result) {
+	ew.println("== Model summary ==")
+	ew.println(strings.Join([]string{"MODEL", "MEAN", "PASSED", "PARTIAL", "FAILED", "ERRORS", "MEAN_LATENCY", "TOTAL_TOKENS", "TOK/S"}, "\t"))
+
+	for _, ms := range summarize(models, results) {
+		row := []string{
+			ms.model,
+			meanCellText(ms.overallMean),
+			fmt.Sprintf("%d", ms.passed),
+			fmt.Sprintf("%d", ms.partial),
+			fmt.Sprintf("%d", ms.failed),
+			fmt.Sprintf("%d", ms.errors),
+			durationText(ms.meanLatency, !math.IsNaN(ms.overallMean)),
+			fmt.Sprintf("%d", ms.totalTokens),
+			rateText(ms.avgTokPerSec),
+		}
+		ew.println(strings.Join(row, "\t"))
+	}
+}